Machine Learning Engineer

We're looking for an MLE to develop & maintain AI-powered internal products, machine learning services, & data-driven automation systems. This role focuses on building practical AI solutions that support business operations — including forecasting, classification, search/recommendation, RAG, OCR, chatbot, and predictive analytics.

You'll work across multiple AI and data domains, from experimentation in notebooks to production-ready APIs and services. This covers the full stack: dataset preparation, model training and evaluation, backend service development, LLM and vector database integration, and deployment.

Job Description

• Own the full ML lifecycle: data ingestion, preprocessing, feature engineering, model training, fine-tuning, evaluation, deployment, and monitoring
• Build and maintain production ML services and inference APIs
• Design and operate RAG pipelines: embeddings generation, vector DB integration, retrieval logic, and LLM orchestration for chatbots and document search
• Prepare, clean, transform, and analyze structured and unstructured datasets
• Train, evaluate, and optimize models for classification, regression, and forecasting use cases
• Collaborate with product, engineering, and stakeholders to translate business needs into AI solutions with clear success metrics
• Produce and maintain clear documentation and runbooks

• Min 2-4 years experience in Machine Learning Engineer, or other related AI positions.
• Strong Python development skills with production-grade coding practices and testing
• Solid experience with data preparation, EDA, feature engineering, and model evaluation
• Demonstrated experience deploying ML models to production (APIs / inference services)
• Experience with SQL and working knowledge of cloud object storage (S3 or equivalent)
• Familiarity with containerization (Docker) and basic orchestration concepts
• Practical experience with NLP workflows: embeddings, LLM APIs, and RAG architectures (embedding models + vector databases)
• Strong ownership and troubleshooting skills — able to operate as a single contributor covering multiple domains
• Clear communicator, able to explain technical tradeoffs to non-technical stakeholders